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Identification of practitioners at high risk of complaints to health profession regulators
BACKGROUND: Some health practitioners pose substantial threats to patient safety, yet early identification of them is notoriously difficult. We aimed to develop an algorithm for use by regulators in prospectively identifying practitioners at high risk of attracting formal complaints about health, co...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6567559/ https://www.ncbi.nlm.nih.gov/pubmed/31196074 http://dx.doi.org/10.1186/s12913-019-4214-y |
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author | Spittal, Matthew J. Bismark, Marie M. Studdert, David M. |
author_facet | Spittal, Matthew J. Bismark, Marie M. Studdert, David M. |
author_sort | Spittal, Matthew J. |
collection | PubMed |
description | BACKGROUND: Some health practitioners pose substantial threats to patient safety, yet early identification of them is notoriously difficult. We aimed to develop an algorithm for use by regulators in prospectively identifying practitioners at high risk of attracting formal complaints about health, conduct or performance issues. METHODS: Using 2011—2016 data from the national regulator of health practitioners in Australia, we conducted a retrospective cohort study of 14 registered health professions. We used recurrent-event survival analysis to estimate the risk of a complaint and used the results of this analysis to develop an algorithm for identifying practitioners at high risk of complaints. We evaluated the algorithm’s discrimination, calibration and predictive properties. RESULTS: Participants were 715,415 registered health practitioners (55% nurses, 15% doctors, 6% midwives, 5% psychologists, 4% pharmacists, 15% other). The algorithm, PRONE-HP (Predicted Risk of New Event for Health Practitioners), incorporated predictors for sex, age, profession and specialty, number of prior complaints and complaint issue. Discrimination was good (C-index = 0·77, 95% CI 0·76–0·77). PRONE-HP’s score values were closely calibrated with risk of a future complaint: practitioners with a score ≤ 4 had a 1% chance of a complaint within 24 months and those with a score ≥ 35 had a higher than 85% chance. Using the 90th percentile of scores within each profession to define “high risk”, the predictive accuracy of PRONE-HP was good for doctors and dentists (PPV = 93·1% and 91·6%, respectively); moderate for chiropractors (PPV = 71·1%), psychologists (PPV = 54·9%), pharmacists (PPV = 39·9%) and podiatrists (PPV = 34·0%); and poor for other professions. CONCLUSIONS: The performance of PRONE-HP in predicting complaint risks varied substantially across professions. It showed particular promise for flagging doctors and dentists at high risk of accruing further complaints. Close review of available information on flagged practitioners may help to identify troubling patterns and imminent risks to patients. |
format | Online Article Text |
id | pubmed-6567559 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-65675592019-06-17 Identification of practitioners at high risk of complaints to health profession regulators Spittal, Matthew J. Bismark, Marie M. Studdert, David M. BMC Health Serv Res Research Article BACKGROUND: Some health practitioners pose substantial threats to patient safety, yet early identification of them is notoriously difficult. We aimed to develop an algorithm for use by regulators in prospectively identifying practitioners at high risk of attracting formal complaints about health, conduct or performance issues. METHODS: Using 2011—2016 data from the national regulator of health practitioners in Australia, we conducted a retrospective cohort study of 14 registered health professions. We used recurrent-event survival analysis to estimate the risk of a complaint and used the results of this analysis to develop an algorithm for identifying practitioners at high risk of complaints. We evaluated the algorithm’s discrimination, calibration and predictive properties. RESULTS: Participants were 715,415 registered health practitioners (55% nurses, 15% doctors, 6% midwives, 5% psychologists, 4% pharmacists, 15% other). The algorithm, PRONE-HP (Predicted Risk of New Event for Health Practitioners), incorporated predictors for sex, age, profession and specialty, number of prior complaints and complaint issue. Discrimination was good (C-index = 0·77, 95% CI 0·76–0·77). PRONE-HP’s score values were closely calibrated with risk of a future complaint: practitioners with a score ≤ 4 had a 1% chance of a complaint within 24 months and those with a score ≥ 35 had a higher than 85% chance. Using the 90th percentile of scores within each profession to define “high risk”, the predictive accuracy of PRONE-HP was good for doctors and dentists (PPV = 93·1% and 91·6%, respectively); moderate for chiropractors (PPV = 71·1%), psychologists (PPV = 54·9%), pharmacists (PPV = 39·9%) and podiatrists (PPV = 34·0%); and poor for other professions. CONCLUSIONS: The performance of PRONE-HP in predicting complaint risks varied substantially across professions. It showed particular promise for flagging doctors and dentists at high risk of accruing further complaints. Close review of available information on flagged practitioners may help to identify troubling patterns and imminent risks to patients. BioMed Central 2019-06-13 /pmc/articles/PMC6567559/ /pubmed/31196074 http://dx.doi.org/10.1186/s12913-019-4214-y Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Spittal, Matthew J. Bismark, Marie M. Studdert, David M. Identification of practitioners at high risk of complaints to health profession regulators |
title | Identification of practitioners at high risk of complaints to health profession regulators |
title_full | Identification of practitioners at high risk of complaints to health profession regulators |
title_fullStr | Identification of practitioners at high risk of complaints to health profession regulators |
title_full_unstemmed | Identification of practitioners at high risk of complaints to health profession regulators |
title_short | Identification of practitioners at high risk of complaints to health profession regulators |
title_sort | identification of practitioners at high risk of complaints to health profession regulators |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6567559/ https://www.ncbi.nlm.nih.gov/pubmed/31196074 http://dx.doi.org/10.1186/s12913-019-4214-y |
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